Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
Med Decis Making. 2020 Jan;40(1):106-111. doi: 10.1177/0272989X19894940.
Individual-level simulation models are used to assess the effects of health interventions in complex settings. However, estimating valid causal effects using these models requires correct parametrization of the relationships between time-varying treatments, outcomes, and other variables in the causal structure. To parameterize these relationships, individual-level simulation models typically need estimates of the direct effects of treatment. However, direct effects of treatment are often not well- defined and therefore cannot be validly estimated from any data. In this paper, we explain the causal meaning of the parameters of individual-level simulation models as direct effects, describe why direct effects may be difficult to define unambiguously in some settings, and conclude with some suggestions for the design of individual-level simulation models in those settings.
个体水平模拟模型被用于评估复杂环境下卫生干预措施的效果。然而，使用这些模型来估计有效的因果效应，需要正确参数化随时间变化的处理、结果和因果结构中其他变量之间的关系。为了参数化这些关系，个体水平模拟模型通常需要处理的直接效应的估计。然而，处理的直接效应通常没有很好的定义，因此不能从任何数据中有效地估计。在本文中，我们将解释个体水平模拟模型参数的因果意义作为直接效应，描述为什么在某些情况下直接效应可能难以明确界定，并对在这些情况下设计个体水平模拟模型提出一些建议。
